The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm
The government modernizes the state revenue system by launching the State Revenue Module (MPN), which connects the billing code generation system with the payment system at the collecting agent. This study aims to develop an information system on how to pay state revenues to assist the public in depositing state revenues through payment channels at the collecting agent by applying the Finite State Automata (FSA) modeling concept. The system development uses the waterfall model while collecting the agent's data. The FSA design and testing phase uses the JFLAP application, while the application design stage uses the Laravel framework. System testing uses the black box testing method to determine how far the functioning of the components or menus on the system has come. In addition to making it easier for the public to deposit state revenues, this study also shows that automata theory can help design a payment information application system. This application design offers a website display of information on how to pay state revenues that can run well. Every menu on the system, when used, has no errors so that this system can be utilized by users and developed in applications based on Android and iOS.
Bisnis merupakan kegiatan yang tidak pernah berhenti, segala sesuatu yang menjadi peluang usaha pun dapat dijadikan bisnis yang menjanjikan kepada pelaku bisnis, semakin maju dan berkembangnya dunia usaha, membuat sebagian dari pebisnis gulung tikar, banyak faktor yang membuat mereka kesusahan dalam mempertahankan bisnisnya, diantaranya adalah pelanggan. Penelitian ini bertujuan untuk menentukan pelanggan potensial dan loyal kepada pelaku usaha, pelanggan yang potensial ditentukan dengan segmentasi pelanggan. Model RFM (Recency, Frequency, Monetary) digunakan untuk mencari atribut yang cocok untuk segementasi pelangan dan melalukan klastering menggunakan algoritma K-Means, model yang di keluarkan oleh K-Means pelanggan yang potensial memiliki nilai frekuensi yang besar. Menggunakan Davies bouldin index untuk membantu tingkat akurasi pada data klister.
Analysis of hotel review sentiment is very helpful to be used as a benchmark or reference for making hotel business decisions today. However, all the review information obtained must be processed first by using an algorithm. The purpose of this study is to compare the Classification Algorithm of Machine Learning to obtain information that has a better level of accuracy in the analysis of hotel reviews. The algorithm that will be used is k-NN (k-Nearest Neighbor) and NB (Naive Bayes). After doing the calculation, the following accuracy level is obtained: k-NN of 60,50% with an AUC value of 0.632 and NB of 85,25% with an AUC value of 0.658. These results can be determined by the right algorithm to assist in making accurate decisions by business people in the analysis of hotel reviews using the NB Algorithm.
Spesialisasi jurusan menjadi hal penting bagi mahasiswa untuk menentukan outline tugas akhir. Dalam penulisan ini kami membuat prediksi peminatan berdasarkan data nilai matakuliah terkait. Penulis menggunakan metode Jaringan Syaraf Tiruan (Artificial Neural Network) yang menghasilkan model seperti syaraf otak manusia berupa neuron dalam memecahkan masalah. Berdasarkan hasil pengumpulan data dan sudah melewati proses pemilihan data (Preproccessing), selanjutnya diproses menggunakan software rapidminer didapatkan keluaran berupa data neuralnet yang menghasilkan output dan performance dengan tingkat akurasi sebesar 85,97% dan nilai AUC sebesar 0,801. Diharapkan dengan adanya hasil ini dapat program studi atau himpunan bisa menentukan focus terhadap mahasiswanya dan dapat dikembangkan lagi dalam penelitian berikutnya.Kata kunci: jaringan syaraf tiruan, proses, data
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